Enhanced Artificial Bee Colony with Savings Algorithm for Inventory Routing Problem

Abstract

Inventory Routing Problem is a critical component of Supply Chain Management, where it is a coordination of inventory management and transportation. It aims to balance the trade-off between transportation costs for delivering products and holding costs for maintaining inventory. Several real-world problems faced nowadays require effective optimization and logistical solutions, where this problem arises in various industries and has become increasingly complex. The problem addressed in this study is based on an automotive parts supply chain that consists of a depot, an assembly plant, a set of homogeneous capacitated vehicles, and multi-suppliers on a finite horizon with multi-periods. Artificial Bee Colony is a swarm intelligence algorithm that is based on the behaviour of bees in a colony, where information is shared through waggle dance. ABC consists of three phases, which are employed bee phase, onlooker bee phase, and scout bee phase. This study proposed an enhancement in the initialization phase and in onlooker bee phase of the ABC algorithm. Clarke Wright savings algorithm was implemented in the initialization phase to determine the best feasible delivery routes while minimizing the total transportation cost. 2-opt and 2-opt(asterisk) were used to improve the routes in the onlooker bee phase. Results showed that 7 better total cost were found out of 14 benchmark datasets when compared to the previous literature. The enhanced ABC algorithm obtained better results with 5.59 percent at most, which demonstrated the effectiveness of the algorithm.